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Assessment of Future Climate and its Impact on Streamflow: a Case Study of Bagmati Basin, Nepal

Examination Committee: Dr. Mukand S. Babel (Chairperson) Dr. Sylvain Roger PerretDr. Roberto S. ClementeProf. Ashim Das Gupta

Shyam Prasad Bhusal (ID: st107394)

WEM/SET

2Introduction

Outline Outline

Introduction

Methodology

Study Area and Data Collection

Results and Discussion

Conclusions and Recommendations

3IntroductionRationale of the Study Rationale of the Study

Climate change is global phenomenon but its trend and extent varies spatially and temporarily(IPCC,2007) bringing uncertainties in local climate.

Changes in precipitation and temperature brings uncertainties in basin hydrology.

Uncertainties of future water availability and extreme events .

Problem in planning, design and management of water resources (Minville et al., 2008).

To address this problem climate change impact study should be conducted at local level.

GCMs provide future climatic variables but their grid size is very large and data contain bias.

Bagmati river basin in Hadcm3 grid geometry

Hadcm3 grid: 300 by 400 km

4Introduction ObjectivesObjectives

To quantify the future changes in climate and its impact on streamflow in the Bagmati Basin in Nepal. To quantify the future changes in climate and its impact on streamflow in the Bagmati Basin in Nepal.

Overall Objective

1. To estimate future local climate with downscaling of GCM predicted climatic variables.

2. To analyze present and future trends of extreme climate indices

3. To quantify the impact of climate change in streamflow and future water availability.

1. To estimate future local climate with downscaling of GCM predicted climatic variables.

2. To analyze present and future trends of extreme climate indices

3. To quantify the impact of climate change in streamflow and future water availability.

Sub - objectives

5IntroductionScope and Limitations of the StudyScope and Limitations of the Study

Collection of required spatial, meteorological and hydrological data data

GCM selection based on the statistical analysis

Downscaling of GCM temperature and precipitation using SDSM model

Analysis of future trends temperature of precipitation changes

Analysis of extreme climate indices using WMO guideline

Simulation of future streamflow using hydrological model HEC-HMS.

Collection of required spatial, meteorological and hydrological data data

GCM selection based on the statistical analysis

Downscaling of GCM temperature and precipitation using SDSM model

Analysis of future trends temperature of precipitation changes

Analysis of extreme climate indices using WMO guideline

Simulation of future streamflow using hydrological model HEC-HMS.

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Methodology

7 MethodologyResearch methodology Framework

GCM SelectionGCM Selection

Outcome:Local level GCM precipitation and Temperature (Objective:1)

Outcome:Local level GCM precipitation and Temperature (Objective:1)

Extreme Climate Indices analysis Extreme Climate Indices analysis

Hydrological Modeling Hydrological Modeling

Daily precipitation at present time

Daily precipitation at present time

Downscaled GCM daily precipitationDownscaled GCM daily precipitation

Calibrated Hydrological Model Calibrated Hydrological Model

Present water availability

Present water availability

Future water availability Future water availability

Climate change impact on water availability is inferred

(Objective:3)

Climate change impact on water availability is inferred

(Objective:3)

Statistical Downscaling Statistical Downscaling

Impact of climate change on extreme temperature and precipitation events is inferred (Objective: 2)

Impact of climate change on extreme temperature and precipitation events is inferred (Objective: 2)

8 Methodology (objective:1)Methodology for Downscaling

Input:

-Station meteorological data (Precipitation & Temperature ): Predictand

-NCEP reanalyzed data: Predictors

Input:

-Station meteorological data (Precipitation & Temperature ): Predictand

-NCEP reanalyzed data: Predictors

Input:Screened predictors from GCM (Scenario A2 & B2)

Input:Screened predictors from GCM (Scenario A2 & B2)

Statistical Downscaling Model (SDSM)

Statistical Downscaling Model (SDSM)

Screening of Predictor Variables

Screening of Predictor Variables

Output : Downscaled GCM Data (Precipitation and Temperature) for different periods ( A2, B2 )

Output : Downscaled GCM Data (Precipitation and Temperature) for different periods ( A2, B2 )

Calibration and Validation Calibration and Validation

Predictor-Predictand Relationship

Predictor-Predictand Relationship

- Correlation coefficient

- Scatter plot

Methodology for downscaling with SDSM model

Daily predictor variables

Code

Mean Temperature Temp

Mean Sea Level Pressure Mslp

500 hPa geopotential height P500

850 hPa geopotential height P850

Near surface relative humidity

rhum

Relative humidity at 500hPa height

r500

Relative humidity at 850 hPa height

R850

Near surface specific humidity

Shum

Geostrophic airflow velocity **_f

Vorticity **_z

Zonal velocity component **_u

Meridonal velocity component

**_v

Wind direction **th

Divergence **zh

9Methodology (objective:2)

IDID Indicator NameIndicator Name DefinitionsDefinitions Units Units Temperature Indices

TXx Max Tmax Maximum of Maximum Temp οC

TNx Max Tmin Maximum of minimum Temp ºC

TXn Min Tmax Minimum of maximum Temp ºC

TNn Min Tmin Minimum of minimum Temp ºCTN10p Cold Nights Percentage of days when TN<10th percentile DaysTX10p Cold days Percentage of days when TX<10th percentile DaysTN90p Warm nights Percentage of days when TN>90th percentile DaysTX90p Hot days Percentage of days when TX>90th percentile Days

Precipitation Indices

Rx5day Max 5-day precipitation Monthly maximum consecutive 5-day precipitation mm

R10 Heavy precipitation days Annual count of days when PR >= 10mm Days

R20 Heavy precipitation days Annual count of days when PR >= 20mm Days

R35Very heavy precipitation days

Annual count of days when PR >= 35mm Days

CDD Consecutive dry days Maximum number of consecutive days with PRCP<1mm Days

CWD Consecutive wet daysMaximum number of consecutive days with PRCP>=1mm

Days

R95p Very wet days Annual total PRCP when PR>95th percentile mm

Extreme Climate Indices Extreme Climate Indices

Quantification of Climate Change Impact on Water Quantification of Climate Change Impact on Water ResourcesResources

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Methodology (objective:3)

River Flow Hydrograph (Water availability)

River Flow Hydrograph (Water availability)

HEC-GeoHMS (Arc View GIS 3.2)

HEC-GeoHMS (Arc View GIS 3.2)

Hydrological Model : HEC- HMSHydrological Model : HEC- HMSObserved

precipitation

Input : -DEM & Soil Data-Land Use data

Gauged Discharge

Outcome:1.Watershed Delineation2.Stream Network Development 3.Stream Characteristics4.Watershed Characteristics

Outcome:1.Watershed Delineation2.Stream Network Development 3.Stream Characteristics4.Watershed Characteristics

Model Calibration/Validation

Model Calibration/Validation

Daily precipitation for Simulation period

Study Area: Bagmati River Basin Study Area: Bagmati River Basin

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Study area & Data collection

CHINA

INDIABagmati river basin

Bagmati river basinNepal boundary and districts

Location Map of the Bagmati river basin

Location: Lat 26ο 45’- 27ο 49’ N and Long 85ο 02’- 85ο 57’ E

Basin area: 3759 km2

Altitude: 75 m – 2900 msl

Climate: Sub-tropical to Cold temperate

Upper part: •Kathmandu, Lalitpur and Bhaktapur districts• Area = 662 km2

• 69% of basin population inhabit • Water stress

Middle & lower part: experiencing flood problem

Location: Lat 26ο 45’- 27ο 49’ N and Long 85ο 02’- 85ο 57’ E

Basin area: 3759 km2

Altitude: 75 m – 2900 msl

Climate: Sub-tropical to Cold temperate

Upper part: •Kathmandu, Lalitpur and Bhaktapur districts• Area = 662 km2

• 69% of basin population inhabit • Water stress

Middle & lower part: experiencing flood problem

INDIA

CHINA

Data Collection Data Collection

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Study area & Data collection

Climatic data

Station NameElevation

(msl)Latitude

(oE)Longitude

(oN)Daily

RainfallDaily Min/Max

Temp.Source

Ktm Airport 1336 27o42' 85o22' 1971-2005 1968-2005 DHM

Daman 2314 27o63' 85o05' 1971-2005 1981-2005 DHM

Budhanilkanttha 1350 27o47' 85o22' 1971-2005 1978-2005 DHM

Sankhu 1449 27o45' 85o29' 1971-2005 - DHM

Godavary 1400 27o34' 85o24' 1971-2005 1981-2000 DHM

Khopasi 1517 27o35' 85o31' 1971-2005 - DHM

Hariharpurgadhi 250 27o20' 85o30' 1971-2005 - DHM

Sindhuligadhi 1463 27o17' 85o58' 1971-2005 1978-2000 DHM

Ramolibariya 152 27o01' 85o23' 1971-2005 - DHM

Pattharkot 275 27o05' 85o40' 1971-2005 - DHM

Land use: 35% agriculture, 57

% forestSoil Map :

loamy soil is dominant

DEM of 90 m resolution from CGIAR website

Daily discharge for period (1990-2006)

Station Name Latitude

(oE)Longitude

(oN)

Catchment Area

(Km2 )Source

Pandheradovan 27o06' 85o28'30' 2789.3DHM, Nepal

Spatial data

• GCM data from IPCC-DDC• NCEP predictors from SDSM website

• GCM data from IPCC-DDC• NCEP predictors from SDSM website

Data downloaded from website

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Results and Discussions

14Result & Discussion (objective:1)Downscaling GCM Temperature Downscaling GCM Temperature

Validation of model result for downscaling maximum temperature (1990-2000)

15Result & Discussion (objective:1)Downscaling of GCM Precipitation Downscaling of GCM Precipitation

Comparison of statistics of observed and simulated Precipitation for period 1990-2000

16Result & Discussion (objective:1) Downscaling Performance Downscaling Performance

Statistical performance of downscaled result Statistical performance of downscaled result  Climate Variable

Max. Temp Precipitation

GCM Data SD R2 SD R2

Observed 3.63 - 4.89 -Raw A2 5.46 0.61 14.17 0.40Downscaled A2 3.67 0.89 4.60 0.87

Statistical performance of downscaled result Statistical performance of downscaled result  Climate Variable

Max. Temp Precipitation

GCM Data SD R2 SD R2

Observed 3.63 - 4.89 -RawB2 5.58 0.54 2.91 0.52Downscaled B2 3.91 0.87 4.63 0.81

17Result & Discussion (objective:1) Future scenario of downscaled temperature Future scenario of downscaled temperature

Basin average future changes in maximum temperature (oC) relative to base period

YearWinter Summer

Scenario A2 Scenario B2 Scenario A2 Scenario B22010-2039 (2020s) 0.4 0.4 0.6 0.52040-2069 (2050s) 1.1 0.9 1.2 0.9(2070-2099) 2080s 1.8 1.4 2.3 1.6

Winter: Dec-Feb, Summer: March-June , Base period: 1970-1999 Summer has higher increased rate Spatial variation

18Result & Discussion (objective:1)

Temporal variation in basin average precipitation relative to base period

Basin average changes (%) in annual total precipitation relative to base period

Period Scenario A2 Scenario B2

2020s 7.5 8.3

2050s 12 14.8

2080s 14.7 20.5

19Result & Discussion (objective:1)

Spatial variation in temporal trend of annual precipitation relative to base period

Upper par of basin

20Result & Discussion (objective:2) Basin average trends of extreme temperature indices

TXx = Summer maximum temp extreme , TNx = Summer minimum temp extremeTXn = Winter maximum temp extreme, TNn = Winter minimum temp extreme

21Result & Discussion (objective:2) Basin average trends of extreme precipitation indices

CDD = consecutive dry days

CWD = Consecutive wet days R10 = No of days with precipitation events > 10mm

Rx5day = Maximum 5 day prcp.

22Result & Discussion (objective:3)Hydrological Model Calibration and ValidationHydrological Model Calibration and Validation

Statistical performance during model calibration & Validation

Statistical Parameters Calibration Validation

Coefficient of determination (R2) 0.71 0.66

Nash-Sutcliffe Efficiency (NSE) 0.69 0.66

Volume Error (%) 1.2 -5.78

Calibration (1999-2001) Validation (2002)

23Result & Discussion (objective:3)Scenario Generated to Assess Climate Change ImpactScenario Generated to Assess Climate Change Impact

Scenario runs developed to assess climate change impact on river flow and Scenario runs developed to assess climate change impact on river flow and water availabilitywater availability

Simulation runs with downscaled precipitation

SRESS A2 SRESS B2

Runs Simulation Period Runs Simulation Period

A2-2000 (base period)

1995-2005 B2_2000 1995-2005

A2_2020 2015-2025 B2_2020 2015-2025

A2_2050 2045-2055 B2_2050 2045-2055

A2_2080 2075-2085 B2_2080 2075-2085

24Result & Discussion (objective:3) Climate Change Impact on River Flow HydrographClimate Change Impact on River Flow Hydrograph

Comparison of future flow hydrographs with base period flow hydrograph as predicted by scenario B2

Comparison of future flow hydrographs with base period hydrograph as predicted by scenario A2

25Result & Discussion (objective:3)

Seasonal Variation of Water Availability on Spatial and Seasonal Variation of Water Availability on Spatial and Temporal ScaleTemporal Scale

Scenario A2 (Whole Basin)

Season Future Changes (%) in water availability relative to base period

2020s 2050s 2080s

Pre-monsoon 3.52 -8.90 -12.72

Monsoon 7.77 9.83 18.23

Post-monsoon 0.96 12.05 9.39

Annual 6.38 7.39 12.76

Seasonal changes (%) in water availability relative to base period according to Scenario B2, whole basin

Season Future Changes (%) in water availability relative to base period (2000)

2020s 2050s 2080sPre-monsoon 7.90 12.47 17.45Monsoon 4.01 7.66 14.41Post-monsoon 22.27 9.74 19.17Annual 6.56 8.51 15.33

Scenario A2 (Upper part of basin )

Season

Future Changes (%) in water availability relative to base period

2020s 2050s 2080s

Pre-monsoon -8.85 -9.88 -16.94

Monsoon 1.67 1.30 6.61

Post-monsoon -3.14 -1.34 -12.47

Annual -0.73 -0.96 0.28

• Monsoon: June-Sep• Pre-monsoon: Jan – May• Post monsoon: Oct-Dec

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Conclusions and Recommendations

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ConclusionsConclusions

• Downscaling technique has well estimated the mean and extreme values of temp whereas it could not estimate the extreme precipitation events well.

• Wide variation of future changes in temperature and precipitation within the basin suggests that the impact studies should be conducted in smaller areas.

• Increased heavy precipitation events (R20, R35, Rx5day and R95p) may lead to increased frequency and intensity of floods.

• The increase in CDD and decrease in CWD indicates the occurrence of more intense droughts in the future.

• Summer may have more severe impact of warming than the winter

• Both scenarios A2 and B2 show higher increase in water availability during monsoon indicating increased flood problems in lower part of basin.

• The spatial analysis of Climate change within the basin indicates that the upper part of the basin is expected to be drier than lower part

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Recommendations Recommendations

Recommendations based on Conclusions

Citing the increased water stress in the upper part of the basin during dry period, it is recommended to the water management authorities in the basin to make necessary plans to cope with possible degrading situation of water stress.

The extreme indices analysis shows increase in frequency and intensity of droughts and floods. To mitigate the dire consequences of such extreme events, adaptation strategies should be designed.

Recommendations for Further Studies

Similar study using more GCM and downscaling techniques

Further studies on vulnerability and adaptation using the result of this study.

Climate change impact studies on water quality considering the result of this study.